Using NCAA Stats to Predict NBA Draft Order
Intro & Lit Review Predicting the NBA draft is always difficult. Should you draft a player on college statistics, NCAA tournament performance, combine results, potential, […]
basketball
Can they Keep Up? A Look into the Pace Statistic in the NBA
Analytics is taking over the game of basketball in a beautifully informative way. Every metric sheds new light on a teams performance and provides a […]
Remembering Kobe Through Data
I built this visual to remind us the impact that he had on people off the court. It is easy to quantify his basketball data but his impact as a man should not be overlooked.
Beyond The Arch: Brook Lopez’s Evolution
Beyond The Arch is a series of articles where I use K Means Clustering to better understand how players are used on offense in the […]
How Far Should the NBA 3-Point Line Actually Be
The 3-Point shot has changed the incentive structure of basketball. In this analysis I try to find an optimal distance for the line
Beyond The Arch: A Closer Look at Spot-Up Wings
Taking a deeper dive in to the Spot-Up Wing player archetype
To Start or Not to Start, That is the Question (For the Pelicans)
#1 overall pick of the 2019 draft, Zion Williamson, is currently out with a torn lateral meniscus. With an initial sideline time of 6–8 weeks, […]
Beyond The Arch: A Closer Look at On-Ball Playmakers
In my previous article, I used a K-Means clustering model to develop a brand new group of offensive archetypes to bucket players. The result was an […]
Beyond The Arch: Introducing a New Way to Understand the Game
New Archetypes for NBA players based on play type data.
How to Simulate NBA Games in Python
A quick and dirty overview of monte carlo simulation applied to basketball outcomes